Landmine detection using feedback NQR

Blauch, A.J.; Schiano, J.L.; Ginsberg, Mark D.

doi:10.1117/12.357070

Cited by 5 publications

(2 citation statements)

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“…The strength of these weak induction currents obtained from the sensor coils determines the amount of explosive. In addition, the frequency of the induction currents gives the type of explosive [53] [54]. Because of the nature of the NQR method, low sensitivity that is a result of low resonant frequencies (0.5 -6 MHz) is a big problem [55].…”

Section: Nuclear Quadrupole Resonance (Nqr) Methodsmentioning

confidence: 99%

Performance Analysis of Techniques Used for Determining Land Mines

Ege¹,

Kakilli²,

Kılıç³

et al. 2014

IJG

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Today, remote sensing is used for different methods and different purposes. In all of the detection methods, some considerations such as low energy consumption, low cost, insensitivity to environmental changes, high accuracy, high reliability and robustness become important. Taking into account these facts, remote sensing methods are used in applications such as geological and archeological research, engineering areas, health services, preserving and controlling natural life, determination of underground sources, controlling air, sea and road traffic, military applications, etc. The method to be used is based on the object type to be detected, material to be made, and location to be found. The remote sensing methods from the past up to today can be listed as acoustic and seismic, ground penetration radar (GPR) detection, electromagnetic induction, infrared (IR) imaging, neutron quadrupole resonance (NQR), thermal neutron activation (TNA), neutron back scattering, X-ray back scattering, and magnetic anomaly detection. In these methods, detected raw images have to be processed, filtered and enhanced. In order to achieve these operations, some algorithms are needed to be developed. In this study, the methods used in detecting land mines remotely and their performance analysis have been given. In this way, the last situation on the advantages and disadvantages of methods used, application areas and detection accuracies are determined. Furthermore, the algorithms such as transmission line matrix (TLM), finite difference time-domain (FDTD), the method of moment (MoM), split step parabolic equation (SSPE) and image processing and intelligent algorithms are presented in detail.

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Section: Nuclear Quadrupole Resonance (Nqr) Methodsmentioning

confidence: 99%

Performance Analysis of Techniques Used for Determining Land Mines

Ege¹,

Kakilli²,

Kılıç³

et al. 2014

IJG

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show abstract

“…The mass analysis technique is preferred more often for military purposes because it is faster. Ground penetrating radar [5][6][7][8][9][10][11][12][13], electromagnetic induction [1,14,15], nuclear quadrupole resonance [4,[16][17][18], acoustic seismic [1,[19][20][21][22], and neutron-based measurement [23][24][25][26][27][28] are the most important methods using mass analysis technique. In all of these methods, an underground signal is broadcast and the reflected signal from the buried object is evaluated by the BED system.…”

Section: Introductionmentioning

confidence: 99%

Detection and Imaging of Underground Objects for Distinguishing Explosives by Using a Fluxgate Sensor Array

2019

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Active and passive techniques are two different techniques with which to detect buried explosives. In practice, the most preferred active method works by broadcasting a signal underground. This signal may stimulate the buried explosive and cause it to explode. It is important to eliminate or minimize this drawback to ensure the safety of the detector operator. In this respect, it is important to increase the studies on the passive detection technique which is not currently used in practice. The aim of this study was to passively detect improvised explosive devices without stimulating them, and to classify underground objects as explosive or non-explosive. A fluxgate sensor array having 33 components was used for passive magnetic field measurements, and the nearest neighborhood algorithm was preferred for classifying the resulting data. In experimental studies, 33 different samples having different amounts of ferromagnetic properties were used. Successful imaging and classification were achieved for the measurements up to 20 cm below the surface of soil. Data were recorded as 32 × 25 matrices, and then they were reduced to 32 × 2 matrices having the same features. Samples having explosive properties were distinguished from other underground objects with success rates of 86% and 95% for 32 × 25 and 32 × 2 data matrices, respectively. Classification times for 32 × 25 and 32 × 2 data matrices were 42 ms and 3.62 ms, respectively. For data groups where the best results were obtained for the data matrices, frame numbers classified in one second were calculated as 23.80 and 276.2, respectively. False alarm rate achieved was 5.31%. The experimental results proved the successes of the matrices reduction and classification approach. One of the most common problems encountered in passive detecting techniques is that the sensor position affects the measurements negatively. In this paper, a solution has been proposed for this important problem.

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2009

EM Detection of Concealed Targets

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Landmine detection using feedback NQR

Cited by 5 publications

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Performance Analysis of Techniques Used for Determining Land Mines

Performance Analysis of Techniques Used for Determining Land Mines

Detection and Imaging of Underground Objects for Distinguishing Explosives by Using a Fluxgate Sensor Array

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